{"title":"CMC-MMR: multi-modal recommendation model with cross-modal correction","authors":"","doi":"10.1007/s10844-024-00848-x","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Multi-modal recommendation using multi-modal features (e.g., image and text features) has received significant attention and has been shown to have more effective recommendation. However, there are currently the following problems with multi-modal recommendation: (1) Multi-modal recommendation often handle individual modes’ raw data directly, leading to noise affecting the model’s effectiveness and the failure to explore interconnections between modes; (2) Different users have different preferences. It’s impractical to treat all modalities equally, as this could interfere with the model’s ability to make recommendation. To address the above problems, this paper proposes a <span>M</span>ulti-<span>m</span>odal <span>r</span>ecommendation model with <span>c</span>ross-<span>m</span>odal <span>c</span>orrection (CMC-MMR). Firstly, in order to reduce the effect of noise in the raw data and to take full advantage of the relationships between modes, we designed a cross-modal correction module to denoise and correct the modes using a cross-modal correction mechanism; Secondly, the similarity between the same modalities of each item is used as a benchmark to build item-item graphs for each modality, and user-item graphs with degree-sensitive pruning strategies are also built to mine higher-order information; Finally, we designed a self-supervised task to adaptively mine user preferences for modality. We conducted comparative experiments with eleven baseline models on four real-world datasets. The experimental results show that CMC-MMR improves 6.202%, 4.975% , 6.054% and 11.368% on average on the four datasets, respectively, demonstrates the effectiveness of CMC-MMR.</p>","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"4 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10844-024-00848-x","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Multi-modal recommendation using multi-modal features (e.g., image and text features) has received significant attention and has been shown to have more effective recommendation. However, there are currently the following problems with multi-modal recommendation: (1) Multi-modal recommendation often handle individual modes’ raw data directly, leading to noise affecting the model’s effectiveness and the failure to explore interconnections between modes; (2) Different users have different preferences. It’s impractical to treat all modalities equally, as this could interfere with the model’s ability to make recommendation. To address the above problems, this paper proposes a Multi-modal recommendation model with cross-modal correction (CMC-MMR). Firstly, in order to reduce the effect of noise in the raw data and to take full advantage of the relationships between modes, we designed a cross-modal correction module to denoise and correct the modes using a cross-modal correction mechanism; Secondly, the similarity between the same modalities of each item is used as a benchmark to build item-item graphs for each modality, and user-item graphs with degree-sensitive pruning strategies are also built to mine higher-order information; Finally, we designed a self-supervised task to adaptively mine user preferences for modality. We conducted comparative experiments with eleven baseline models on four real-world datasets. The experimental results show that CMC-MMR improves 6.202%, 4.975% , 6.054% and 11.368% on average on the four datasets, respectively, demonstrates the effectiveness of CMC-MMR.
期刊介绍：
The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems.
These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to:
discover knowledge from large data collections,
provide cooperative support to users in complex query formulation and refinement,
access, retrieve, store and manage large collections of multimedia data and knowledge,
integrate information from multiple heterogeneous data and knowledge sources, and
reason about information under uncertain conditions.
Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces.
The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.
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